Literature DB >> 30554659

Hyperglycemia differentially affects proliferation, apoptosis, and BNIP3 and p53 mRNA expression of human umbilical cord Wharton's jelly cells from non-diabetic and diabetic pregnancies.

José Romo-Yáñez1, Mauricio Domínguez-Castro2, Josiff S Flores-Reyes3, Higinio Estrada-Juárez4, Ismael Mancilla-Herrera5, Jessica Hernández-Pineda5, María Luisa Bazan-Tejeda6, Mónica Aguinaga-Ríos3, Enrique Reyes-Muñoz7.   

Abstract

Diabetes in pregnancy constitutes an unfavorable environment for embryonic and fetal development, where the child has a higher risk of perinatal morbidity and mortality, with high incidence of congenital malformations and predisposition to long-term metabolic diseases that increase with a hypercaloric diet. To analyze whether hyperglycemia differentially affects proliferation, apoptosis, and mRNA expression in cells from children of normoglycemic pregnancies (NGPs) and diabetes mellitus pregnancies (DMPs), we used umbilical cord Wharton jelly cells as a research model. Proliferation assays were performed to analyze growth and determine the doubling time, and the rate of apoptosis was determined by flow cytometry-annexin-V assays. AMPK, BNIP3, HIF1α, and p53 mRNA gene expression was assessed by semi-quantitative RT-PCR. We found that hyperglycemia decreased proliferation in a statistically significant manner in NGP cells treated with 40 mM D-glucose and in DMP cells treated with 30 and 40 mM D-glucose. Apoptosis increased in hyperglycemic conditions in NGP and DMP cells. mRNA expression of BNIP3 and p53 was significantly increased in cells from DMPs but not in cells from NGPs. We found evidence that maternal irregular metabolic conditions, like diabetes with hyperglycemia in culture, affect biological properties of fetal cells. These observations could be a constituent of fetal programming.
Copyright © 2018 Elsevier Inc. All rights reserved.

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Keywords:  Apoptosis; Oxidative stress; Pregnancy in diabetics; Wharton jelly

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Year:  2018        PMID: 30554659     DOI: 10.1016/j.bbrc.2018.12.037

Source DB:  PubMed          Journal:  Biochem Biophys Res Commun        ISSN: 0006-291X            Impact factor:   3.575


  1 in total

1.  Identification of key pathways and genes in polycystic ovary syndrome via integrated bioinformatics analysis and prediction of small therapeutic molecules.

Authors:  Praveenkumar Devarbhavi; Lata Telang; Basavaraj Vastrad; Anandkumar Tengli; Chanabasayya Vastrad; Iranna Kotturshetti
Journal:  Reprod Biol Endocrinol       Date:  2021-02-23       Impact factor: 5.211

  1 in total

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